GPF modeling supported by size-resolved filtration efficiency … · 2019-11-26 · 09/11/2019...
Transcript of GPF modeling supported by size-resolved filtration efficiency … · 2019-11-26 · 09/11/2019...
109/11/2019 MODEGAT 2019
GPF modeling supported bysize-resolved filtration efficiency measurements
Michail Mitsouridis, Grigorios Koltsakis, Zissis SamarasAristotle University ThessalonikiJeremy Gidney, John GoodwinJohnson MattheyChristian MartinAVL List GmbH
• Target: Particle free gasoline engines with cost-effective GPFsHow can we use GPF modeling to support system optimization?
• Background: GPF modeling shares many common features and challenges as the long established DPF modeling.However, there are some important differences:– Lower PM/PN emissions -> operation in depth filtration
– Lack of O2 and NO2 for thermal regeneration
– Higher exhaust gas temperatures -> thermal shock risks
– How to combine TWC functions?
– ...
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Background & motivation
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Spherical collector filtration model
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dgrain =3
2
1−εpore
εporedpore
Agglomerate structure = f(fpart, Rppart, ρppart, Rmc)
• fpart: Particle fractal dimension [-] → agglomerate geometry standardization
• Rppart: Primary particle radius [m]
• ρppart: Primary particle density [kg/m3]
• Rmc: Aggregate cluster mobility radius [m] → the diameter of a spherical particle with the same mobility (ease to have constant motion) as the agglomerate in question
porous wall unit cell
deposit layer
washcoat
grainEwall = 1 − e−ηg
3
2
1−εpore,0
εpore,0
Δw
dgrain,0 , Δw: node thickness
ηg = ηg,0 + 1 − ηg,0 erf3nload
2Wcap , Wcap=
εpore,0 − εpore
εpore,0
ηg,0 = ηD + ηR
ηD = nclean,D ∙ 3.5εpore,0
Ku
1
3PenPe , Pe =
uw∙dgrain,0
Dpart
ηR = nclean,R ∙ 1.5εpore,0
Ku
R2
1+R m, m =3−2∙εpore,0
3∙εpore,0, R =
2Rc
dgrain,0
dpore
dgrain
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Filtration model calibration
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The size-resolved filtration efficiency is well captured by the model for particles > 30 nm.
Uncoated GPF Coated GPF: 50 g/l
nclean,D = 0.35
nclean,R = 5
Particulate Filter Test System• 250 kg/h, 240°C
• GPF cell structure: 12mils, 300cpsi
• Calibration of:
– nclean,D: Diffusion mechanism correction factor
– nclean,R: Interception mechanism correction factor
nclean,D = 0.25
nclean,R = 4.5
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Model application & validation with thinner wall filters
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Uncoated GPFs Coated GPFs: 50 g/l
nclean,D = 0.35
nclean,R = 5nclean,D = 0.25
nclean,R = 4.5
The filtration model is predictive for filters of different wall thickness.
This coating does not have a major impact on filtration efficiency (FE).
Model parameters: based on the calibration with the 12/300 GPF
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Effect of soot loading
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nload = 4.5 nload = 8
The model captures reasonably well the filtration efficiency increase due to progressive soot accumulation.
12mils, 300cpsi
Uncoated GPF Coated GPF: 50 g/l
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Total PN filtration efficiency vs soot loading
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Increasingwall thickness
Increasingwall thickness
The filtration model predicts the rate of FE increase as function of collected soot amount.
Uncoated GPFs Coated GPFs: 50 g/l
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Filtration efficiency prediction in real exhaust
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CC: 50 g/l UF: 100 g/l
Engine sweep tests0-700kg/h
T=300-800°C
2.0 l TGDIengine test bed UF: 100 g/l
CC: 50 g/l
The filtration model performs well in the whole engine map.
Engine sweep tests with two exhaust line configurations.
Initial soot loading: 0 g/l
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Towards predictive filtration modeling
The available database was used to derive correlation functions for the filtration model
tuning parameters
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Pressure drop modelingSoot-free filters
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2.2 l Diesel engine(Daimler OM646)
UncoatedEffect of wall thickness
CoatedEffect of washcoat loading on wall permeability
dP
dw=
ሻμ ∙ v(w
kw
kw = kw,0 ⋅ 1 + C4p0pμ
T
Mg
kw,0: Clean wall permeabilityC4: Slip correction factor
Same kw,0 Increasing kw,0
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Pressure drop modelingSoot loaded filters
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The effect of soot in the wall on deltaP is strongly affected by the microstructure after coating.
The empirical model may have a compromised accuracy in the transition region.
dP
dw=
ሻμ ∙ v(w
kw, kw =
1
1kw,0
+ C1 ⋅ ρP + C2 ⋅ ρP2⋅ 1 + C4
p0pμ
T
Mg
dP
dw=
ሻμ ∙ v(w
kp, kp=
ρp,0 ∗ k0
ρp1 + C4
p0pμ
T
Mg
kw: Soot loaded wall permeabilityρp,l: Reference wall soot loading
ρp,0: Soot reference density
k0: Soot permeability
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Towards predictive pressure drop modeling
Correlations of wall permeability for clean and loaded wall state were derived
for a range of 0 to 500 g/l wall.
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Reference wall soot loading: 2.5 g/lwall
Is it possible to avoid filter permeability recalibration for each new filter configuration, i.e. various washcoat loadings (grams per liter of filter), cell- and wall micro-structures?
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Model application in RDE cycle conditions
(100g/l w/c loading)
UF: 100 g/l
CC: 50 g/l
(50g/l w/c loading)
1.8l TGDI enginechassis dynamometer
RDE protocol
• During the RDE test, soot oxidation may take place during fuel cut-off events and high temperatures.
• The UF: 100 g/l has a lower regeneration potential compared to the CC: 50 g/l filter.
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Combined model validationLoss of filtration efficiency due to wall cleaning
CC: 50 g/l washcoat
PN slip: 14%
0.18
RDE high temperature cycle with preloaded GPF (=0.4 g/l)
PN slip (normalized over total emitted engine out PN) appears after soot
loading goes below ~0.2 g/l
Due to high temperature and frequent fuel cutoffs, soot is
oxidized (simulation result only)
The GPF combined model (filtration, transport, reaction phenomena) manages to predict the observed PN slip
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withfuel cut-off events
withoutfuel cut-off events
To validate that the PN slip is associated with wall cleaning, the test was repeated de-activating fuel cut-off events.
Deactivation of fuel-cut off mode drastically reduces the PN slip.The model predictions are in line with the measured observations.
Verification of modelDeactivation of fuel cutoff
CC: 50 g/l washcoat
PN slip: 2%
PN slip: 14%
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Model application exampleUnderfloor vs close-coupled positioning
CC: 50 g/l washcoat UF: 100 g/l washcoat
0.18
Same RDE cycle run with UF and higher w/c loading filter. Initial soot loadings: CC filter: 0.4 g/l, UF filter: 0.1 g/l
PN slip: 14%
UF filter exposed to lower temperature -> less soot is oxidizedThe remaining soot in the UF filter is sufficient to maintain very low PN slip
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Pressure drop prediction accuracy in RDE cycle
Good prediction accuracy under complex operating conditions
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CC: 50 g/l washcoat UF: 100 g/l washcoat
Data zoom in high flow rate operating mode
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Ash transport testing & modeling
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Ash & soot loading250°C187 kg/h
Layer ashdetachment
Redeposition aslayer ash
Deposition asplug ash
SootAsh
Testing Modeling in axisuite(previously parameterized)
Oven regeneration• 650°C in air• 187kg/h• 3 hours
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Ash loaded model validation
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UF: 50 g/l
Engine sweep tests with filters loaded at various ash levels
The model may be used for lifecycle analysis for various filter designs and driving profiles
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Ash model validation in RDE transient cycles
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23g/l ash
42g/l ash
0g/l ash
UF: 50 g/l
• In view of the increasing gasoline after-treatment requirements and complexity, a practical/balanced modeling approach was developed.
• Based on a small database of calibrated models, correlation functions were developed to support permeability and filtration modeling for various washcoat loadings, cell- and wall micro-structures.
• Due to the low engine-out emissions, it is important to maintain sufficient soot amount in the filter to reduce the risk of PN slip. Regenerations during fuel cutoff are of key importance.
• Inclusion of ash transport modeling allows a life cycle analysis and assessment of GPF designs and application-specific model-based optimization.
• Current-future work: – Improve filtration efficiency predictions for very small particles
– Advanced filtration models accounting for pore size distribution
– Model application in non-uniformly coated substrates: zoning & layering
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Summary & conclusions
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The results were obtained within the project UPGRADE. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 724036.
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Acknowledgements
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We gratefully acknowledge:- the financial support of EU - the great work of our colleagues at LAT/AUTh, AVL and JM involved in the measurements. - The valuable support of NGK Europe GmbH providing many of the test samples used in
this work
Thank you very much!
09/11/2019 23SIA Powertrain, Rouen 2014
MODEGAT 2019
Michail Mitsouridis